Predicting reaction performance in C–N cross-coupling using machine learning

Derek T. Ahneman, Jesús G. Estrada, Shishi Lin, Spencer D. Dreher, Abigail G. Doyle

Research output: Contribution to journalArticlepeer-review

260 Scopus citations

Abstract

Machine learning methods are becoming integral to scientific inquiry in numerous disciplines. We demonstrated that machine learning can be used to predict the performance of a synthetic reaction in multidimensional chemical space using data obtained via high-throughput experimentation. We created scripts to compute and extract atomic, molecular, and vibrational descriptors for the components of a palladium-catalyzed Buchwald-Hartwig cross-coupling of aryl halides with 4-methylaniline in the presence of various potentially inhibitory additives. Using these descriptors as inputs and reaction yield as output, we showed that a random forest algorithm provides significantly improved predictive performance over linear regression analysis. The random forest model was also successfully applied to sparse training sets and out-of-sample prediction, suggesting its value in facilitating adoption of synthetic methodology.

Original languageEnglish (US)
Pages (from-to)186-190
Number of pages5
JournalScience
Volume360
Issue number6385
DOIs
StatePublished - Apr 13 2018

All Science Journal Classification (ASJC) codes

  • General

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